1. Technical Field
The present invention relates to image processing, and more particularly, to a system and method for extracting an object of interest from an image using a robust active shape model.
2. Discussion of the Related Art
An active shape model represents a parametric deformable model where a statistical model of a global shape variation from a training set is to be built. This model is used to fit another model to unseen occurrences of an object earlier annotated in the training set. To accomplish this, a model of a shape of interest is learned by collecting a set of training examples and aligning them in a rigid fashion using predefined landmark points corresponding to the shape. Once the shapes have been aligned, a principal component analysis is used to determine the principal modes of variation in addition to the mean average shape. The resulting model may then be used for segmentation.
For example, given a new image, the shapes can be localized by undergoing an iterative segmentation process for locating quality feature points. However, at each iteration, the decision for locating quality feature points is made based on a local search in a direction perpendicular to the model. Although this may be acceptable in a clean image, it is susceptible to break down when an image is very noisy or the boundary of an object is poorly defined.
One of the basic building blocks in any point-based registration scheme involves matching feature points that are extracted from a sensed image to their counterparts in a reference image. Given two sets of points, the goal is to determine the affine transformation that transforms one point set so that its distance from the other point set is minimized. One technique for determining the affine transformation is known as robust point matching.
Robust point matching involves aligning two arbitrary sets of points by establishing a geometric mapping that superimposes the two sets of points in the same reference frame and rejects outliers. Although, robust point matching is capable of establishing a large number of correspondences between two sets of points while rejecting outliers, there is no constraint introduced to limit the amount of deformations. Accordingly, there is a need for a technique of matching two sets of points while limiting the amount of deformations to constrain the deformed set to belong to a class of desired objects.